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Pandas Test

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Type

Software Skills

Time

10 minutes

Level

Medium

Questions

10

About the Test

Pandas is a software library in the Python programming language that is commonly used for data analysis and manipulation. It provides tools and data structures for working with structured data, such as tables or data frames, and includes functions for reading and writing data from a variety of sources, such as CSV files, Excel sheets, and databases.

Pandas is designed to make it easier for developers to work with structured data in Python, and it is widely used in the data science community for tasks such as data preparation, data cleaning, data exploration, and data visualization. It is an open-source library and is available for free under the three-clause BSD license.

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Skills Measured

  • Data Structures
  • Basic functions to view data
  • Data Indexing and Slicing
  • Working with missing data
  • Combining Data

Roles

  •  Data Analyst
  • Data Scientists
  • Statistician

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1

Data Structures

Pandas provide several data structures for storing and manipulating data in tables or data frames. The two primary data structures in pandas are the Series and DataFrame. A Series is a one-dimensional array-like object that can hold any data type, while a DataFrame is a two-dimensional data table with rows and columns. Series and DataFrame objects have many built-in methods for manipulating and accessing data.

2

Basic functions to view data

Pandas provide several functions for quickly viewing and inspecting data in a Series or DataFrame. Some standard functions for viewing data include head(), which returns the first n rows of a DataFrame (default is 5), and tail(), which returns the last n rows of a DataFrame (default is 5). Other useful functions for inspecting data include info(), which provides information about the data type and memory usage of each column, and describe(), which generates summary statistics for numerical columns.

3

Data Indexing and Slicing

Data Indexing and Slicing provide a variety of ways to index and slice data in a Series or DataFrame. This can be done using either integer-based or label-based indexing and can be used to select specific rows, columns, or cells within a DataFrame.

4

Working with missing data

Pandas provide functions and methods for handling missing data, which is data that is unavailable or represented as a placeholder such as NaN (not a number). This includes functions for identifying missing data, replacing missing values with a specific value or a calculated value, and dropping rows or columns with missing data.

5

Combining Data in pandas

Pandas provide several functions for combining or merging data from multiple sources into a single DataFrame. This can be useful when working with data from different sources that need to be combined or when you want to add additional columns or data to an existing DataFrame. Some standard functions for combining data include Concat (), which combines data vertically or horizontally, merge(), which combines data based on common columns or keys, and join(), which combines information based on the index of the DataFrame.

The test is created by a subject-matter expert

Testlify’s skill tests are designed by experienced SMEs (subject matter experts). We evaluate these experts based on specific metrics such as expertise, capability, and their market reputation. Prior to being published, each skill test is peer-reviewed by other experts and then calibrated based on insights derived from a significant number of test-takers who are well-versed in that skill area. Our inherent feedback systems and built-in algorithms enable our SMEs to refine our tests continually.

View sample score cards

Top five hard skills interview questions for Pandas

1. Can you explain how you would use Pandas to load and manipulate data from a CSV file?
Why this Matters?

Loading and manipulating data is a common task for data professionals, and Pandas is a widely-used library for data manipulation. A Pandas Test professional must be proficient in using Pandas to load and manipulate data from various sources.

What to listen for?
  1. Listen for the candidate’s ability to explain how they would use Pandas to load and manipulate data from a CSV file. A good answer should demonstrate a clear understanding of the basic functions and syntax of Pandas, as well as an ability to efficiently manipulate and clean the data.
2. How would you use Pandas to merge two datasets based on a common column?
Why this Matters?

Merging datasets is a common task in data analysis, and Pandas provides powerful functionality for merging datasets. A Pandas Test professional must be able to use Pandas to effectively merge datasets based on a common column.

What to listen for?

Listen for the candidate’s ability to describe their process for merging two datasets based on a common column using Pandas. A good answer should demonstrate a clear understanding of the merge function in Pandas and how to use it to merge datasets.

3. Can you explain how you would use Pandas to group data by a specific column and aggregate values?
Why this Matters?

Grouping and aggregating data is a common task in data analysis, and Pandas provides powerful functionality for grouping and aggregating data. A Pandas Test professional must be able to use Pandas to effectively group and aggregate data.

What to listen for?

Listen for the candidate’s ability to explain how they would use Pandas to group data by a specific column and aggregate values. A good answer should demonstrate a clear understanding of the groupby function in Pandas and how to use it to group and aggregate data.

4. Can you describe how you would use Pandas to handle missing or null values in a dataset?
Why this Matters?

Handling missing or null values is a critical part of data analysis, and Pandas provides functionality for effectively handling missing or null values. A Pandas Test professional must be able to use Pandas to handle missing or null values in a dataset.

What to listen for?

Listen for the candidate’s ability to describe their process for handling missing or null values in a dataset using Pandas. A good answer should demonstrate a clear understanding of the various methods for handling missing or null values in Pandas, as well as an ability to efficiently clean and prepare the data for analysis.

5. Can you explain how you would use Pandas to perform time-series analysis on a dataset?
Why this Matters?

Time-series analysis is a common task in data analysis, and Pandas provides powerful functionality for time-series analysis. A Pandas Test professional must be able to use Pandas to effectively perform time-series analysis on a dataset.

What to listen for?

Listen for the candidate’s ability to explain how they would use Pandas to perform time-series analysis on a dataset. A good answer should demonstrate a clear understanding of the time-series functionality in Pandas, as well as an ability to efficiently analyze and visualize the time-series data.

Frequently Asked Questions for Pandas

This Pandas test looks at candidates’ understanding and abilities in Data Structures, Data Indexing, and Slicing, Basic functions to view data, working with missing data, and combining Data. Pandas is a software library in the Python programming language that is commonly used for data analysis and manipulation. It provides tools and data structures for working with structured data, such as tables or data frames, and includes functions for reading and writing data from a variety of sources, such as CSV files, Excel sheets, and databases.

This test can help you identify individuals with prior experience in Python and pandas to perform data-wrangling techniques, Data Structures, Combining Data, and Data Indexing and Slicing.

  •  Data Analyst
  • Data Scientists
  • Statistician

  • Data Structures
  • Basic functions to view data
  • Data Indexing and Slicing
  • Working with missing data
  • Combining Data

  • Providing data structures for storing and manipulating data in the form of tables or data frames.
  • Providing functions for reading and writing data from and to a variety of sources, such as CSV files, Excel sheets, and databases.
  • Providing tools for cleaning and preparing data for analysis, including functions for handling missing values, duplicates, and other common data issues.
  • Providing functions for performing statistical analysis and data visualization on structured data.

Frequently Asked Questions (FAQs)

Want to know more about Testlify? Here are answers to the most commonly asked questions about our company.

These are ready-made tests, existing in our test library, created by subject matter experts. We have 200+ such skills tests covering various skills from programming skills to DevOps, including Aptitude and Personality testing.

You can go to the ‘Test Library’ tab and search for tests from the Job Role or Test Type drop-down menu.

Currently, we do not offer any sample tests. However, when you select a test, there would be a few practice questions before the start of the actual test, which would give you a fair idea of how the entire test would look.

Our tests typically take between 25-30 minutes to complete.

In case you need to test for a unique skill-set or specialized experience, you can add your own questions and customize the test to suit your requirements.

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